Landslide Monitoring System Implementing IOT Using Video Camera

Aggarwal, Shivam; Mishra, Praveen Kumar; Sumakar, K V S; Chaturvedi, Pratik

doi:10.1109/i2ct.2018.8529424

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…Aggarwal et al [ 94 ] proposed a landslide EW system that uses cameras to detect motion. They use a fairly simple motion detection algorithm so that it can be implemented on a Raspberry Pi connected to the cameras to detect landslides on-site.…”

Section: Landslidesmentioning

confidence: 99%

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

Esposito

Palma

Belli

et al. 2022

Sensors

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Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.

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Section: Landslidesmentioning

confidence: 99%

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

Esposito

Palma

Belli

et al. 2022

Sensors

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“…Their ease of use and affordability means they have also been used extensively as field sensors in the geosciences (Ferdoush and Li, 2014) as the quality of their camera sensors have developed to a science-grade level (Pagnutti et al, 2017). In hazard management, Raspberry Pi cameras have been used as standalone monitoring systems to complement wider internet-of-things (IoT) networks (Aggarwal et al, 2018) and attached to UAVs to produce orthophotographs (Piras et al, 2017). In glacierised environments, the durability, low-cost, and low power requirements of Raspberry Pis means they have been used to complement sensor networks, such as controlling the capture of DSLR-grade timelapse cameras (Carvallo et al, 2017;Giordan et al, 2020) or as a ground station for UAV-based research (Chakraborty et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts

Taylor

Quincey²,

Smith³

2022

Preprint

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Abstract. Glacier calving fronts are highly dynamic environments that are becoming ubiquitous as glaciers recede and, in many cases, develop proglacial lakes. Monitoring of calving fronts is necessary to fully quantify the glacier ablation budget and to warn downstream communities of the threat of hazards, such as glacial lake outburst floods (GLOFs). Timelapse camera arrays, with structure-from-motion photogrammetry, can produce regular 3D models of glaciers to monitor changes in the ice, but are seldom incorporated into monitoring systems owing to the high cost of equipment. In this proof-of-concept study at Fjallsjökull, Iceland, we present and test a low-cost camera system based on Raspberry Pi computers and compare the resulting point cloud data to a reference cloud generated using an unoccupied aerial vehicle (UAV). The mean absolute difference between the Raspberry Pi and UAV point clouds is found to be 0.301 m with a standard deviation of 0.738 m. We find that high-resolution point clouds can be robustly generated from cameras positioned up to 1.5 km from the glacier (mean absolute difference 0.341 m, standard deviation 0.742 m). Combined, these experiments suggest that for monitoring calving events in glaciers, Raspberry Pi cameras represent an affordable, flexible, and practical option for future scientific research. Owing to the connectivity capabilities of Raspberry Pi computers, this opens the possibility for real-time photogrammetry of glacier calving fronts for deployment as an early warning system to calving-triggered GLOFs.

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“…While the factors that cause landslides are geological conditions and morphological conditions. Generally, the definition of a landslide disaster is a condition of moving slope material in the form of rocks or soil from the top to the bottom or it can be said to be out of the slope forming [1] . In principle, landslides can occur when there is a pushing force at the top of the slope that is greater than the resisting force.…”

Section: Introductionmentioning

confidence: 99%

Design of a Prototype of Soil Condition Monitoring System Based on Telegram Messenger Bot

2022

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Balikpapan City has a moderate level of vulnerability to landslides. This is because the city of Balikpapan has the triggering factors and causes of landslides. The trigger factor is a tropical climate with high rainfall of 2,887 mm/year. While the causative factor is that 85% of the Balikpapan City area consists of hilly areas. Therefore, it is necessary to conduct research monitoring soil conditions. This study uses several sensors such as the MPU6050 sensor, Capacitive Soil Moisture V1.2 sensor, buzzer, and the NodeMcu ESP8266 microcontroller. This prototype acts as an early warning system. The buzzer on the prototype will sound if the landslide hazard condition is identified. In addition, this prototype sends data on soil conditions via the Telegram Messenger Bot on the Telegram Messenger application. The results of data collection when the slope of the soil is 10o to 30o, the condition of the soil does not vibrate, and the soil moisture is dry is that there is no landslide. However, when the slope is 40o to 50o and the soil moisture is dry, the result is a landslide. For the data results when the slope is 10-40o, the soil condition does not vibrate, and the soil moisture is moist, there is no landslide. However, at a slope of 50o and moist soil moisture, landslides occur. For data results with a slope of 10o - 50o, the condition of the soil does not vibrate and the moisture of the wet soil is that there is no landslide. For the data results when water is flowed directly using a water hose at a slope of 10o to 30o and the ground does not vibrate is that there is no landslide. However, at a slope of 40o to 50o, the ground does not vibrate and landslides occur.

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Landslide Monitoring System Implementing IOT Using Video Camera

Cited by 17 publications

References 7 publications

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts

Design of a Prototype of Soil Condition Monitoring System Based on Telegram Messenger Bot

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